1. Field of the Invention
The present invention relates generally to a defective pixel removal apparatus and method, and more particularly to an apparatus and method for removing defective pixels through the use of a signal processing scheme.
2. Description of the Related Art
In general, an image sensor, such as a Complementary Metal-Oxide Semiconductor (CMOS), a Charge Coupled Device (CCD), and the like, is used to acquire an image. In such an image sensor, it is difficult for all pixels to have a uniform characteristic due to various differences in the manufacturing process. Such lack of uniformity causes pixels to have an uneven characteristic. Defective pixels generated in this way are classified into two types: “white pixels” which are pixels significantly brighter than neighboring pixels and “black pixels” which are pixels significantly darker than neighboring pixels, wherein the white and black pixels are called “defective pixels” because they are definitely distinguished from neighboring pixels. Therefore, it is considered as an important task to develop a technology to detect a defective pixel and to compensate for the defective pixel through signal processing.
Methods of removing such a defective pixel include a method of removing Laplacian noise, a method of using a weighted mean filter, a method of using a multi-directional median filter, etc.
The method of removing Laplacian noise, which is designed in consideration of the fact that a defective pixel is modeled as Laplacian noise, is effective in removing Laplacian noise such as defective pixels, and preserves edges well. However, the method of removing Laplacian noise has a problem in that, when an edge is thin, the edge is recognized as noise, and thus is removed.
The method of using a weighted mean filter requires an additional processor for determining if a center corresponds to a defective pixel, wherein it is determined if a pixel corresponds to a defective pixel through the use of a predetermined threshold value. Here, because the threshold value is determined according to a degree of noise, an accurate modeling of noise is required. That is, the capability for effective removal of defective pixels is determined depending on a degree of accuracy in determining if a pixel is a defective pixel and on a method used to remove defective pixels.
The method of using a multi-directional median filter is a method of removing defective pixels, without damaging thin edges. However, when the capability of a corresponding image sensor is deteriorated, and defective pixels consecutively appear, the consecutive defective pixels are recognized as edges, and thus are not removed.
As described above, the conventional methods for removing defective pixels have problems in that it is impossible to detect consecutive defective pixels, an edge is damaged when a normal pixel in the edge is classified as a defective pixel, and also a boundary region in an image is indistinctly expressed to lower the reliability when an edge is thin. Accordingly, it is necessary to develop a method for efficiently removing consecutive defective pixels and accurately removing only defective pixels at the same time.